Opus Mt Tc Big En Tr

🚀 opus-mt-tc-big-en-tr

这是一个用于从英语（en）到土耳其语（tr）翻译的神经机器翻译模型。该模型属于 [OPUS - MT 项目](https://github.com/Helsinki - NLP/Opus - MT) 的一部分，此项目致力于让神经机器翻译模型在全球多种语言中广泛可用且易于获取。所有模型最初都使用 [Marian NMT](https://marian - nmt.github.io/) 这一出色的框架进行训练，它是一个用纯 C++ 编写的高效神经机器翻译实现。这些模型通过 huggingface 的 transformers 库转换为 PyTorch 格式。训练数据来自 OPUS，训练流程采用 [OPUS - MT - train](https://github.com/Helsinki - NLP/Opus - MT - train) 的方法。

• 相关出版物：[OPUS - MT – Building open translation services for the World](https://aclanthology.org/2020.eamt - 1.61/) 和 [The Tatoeba Translation Challenge – Realistic Data Sets for Low Resource and Multilingual MT](https://aclanthology.org/2020.wmt - 1.139/)（如果使用此模型，请引用这些文献）

@inproceedings{tiedemann - thottingal - 2020 - opus,
    title = "{OPUS}-{MT} {--} Building open translation services for the World",
    author = {Tiedemann, J{\"o}rg  and Thottingal, Santhosh},
    booktitle = "Proceedings of the 22nd Annual Conference of the European Association for Machine Translation",
    month = nov,
    year = "2020",
    address = "Lisboa, Portugal",
    publisher = "European Association for Machine Translation",
    url = "https://aclanthology.org/2020.eamt - 1.61",
    pages = "479--480",
}

@inproceedings{tiedemann - 2020 - tatoeba,
    title = "The Tatoeba Translation Challenge {--} Realistic Data Sets for Low Resource and Multilingual {MT}",
    author = {Tiedemann, J{\"o}rg},
    booktitle = "Proceedings of the Fifth Conference on Machine Translation",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2020.wmt - 1.139",
    pages = "1174--1182",
}

📚 详细文档

模型信息

使用说明

💻 使用示例

基础用法

from transformers import MarianMTModel, MarianTokenizer

src_text = [
    "I know Tom didn't want to eat that.",
    "On Sundays, we would get up early and go fishing."
]

model_name = "pytorch - models/opus - mt - tc - big - en - tr"
tokenizer = MarianTokenizer.from_pretrained(model_name)
model = MarianMTModel.from_pretrained(model_name)
translated = model.generate(**tokenizer(src_text, return_tensors="pt", padding=True))

for t in translated:
    print( tokenizer.decode(t, skip_special_tokens=True) )

# 预期输出:
#     Tom'un bunu yemek istemediğini biliyorum.
#     Pazar günleri erkenden kalkıp balık tutmaya giderdik.

高级用法

from transformers import pipeline
pipe = pipeline("translation", model="Helsinki - NLP/opus - mt - tc - big - en - tr")
print(pipe("I know Tom didn't want to eat that."))

# 预期输出: Tom'un bunu yemek istemediğini biliyorum.

基准测试

• 测试集翻译结果：[opusTCv20210807 + bt_transformer - big_2022 - 02 - 25.test.txt](https://object.pouta.csc.fi/Tatoeba - MT - models/eng - tur/opusTCv20210807 + bt_transformer - big_2022 - 02 - 25.test.txt)
• 测试集得分：[opusTCv20210807 + bt_transformer - big_2022 - 02 - 25.eval.txt](https://object.pouta.csc.fi/Tatoeba - MT - models/eng - tur/opusTCv20210807 + bt_transformer - big_2022 - 02 - 25.eval.txt)
• 基准测试结果：benchmark_results.txt
• 基准测试输出：benchmark_translations.zip

致谢

这项工作得到了以下项目的支持：

• [欧洲语言网格](https://www.european - language - grid.eu/) 的 [试点项目 2866](https://live.european - language - grid.eu/catalogue/#/resource/projects/2866)。
• [FoTran 项目](https://www.helsinki.fi/en/researchgroups/natural - language - understanding - with - cross - lingual - grounding)，由欧洲研究理事会（ERC）在欧盟的“地平线 2020”研究与创新计划（资助协议编号 771113）下资助。
• MeMAD 项目，由欧盟的“地平线 2020”研究与创新计划资助（资助协议编号 780069）。

我们也感谢 CSC -- 芬兰科学信息技术中心 提供的慷慨计算资源和 IT 基础设施。

模型转换信息

📄 许可证

本模型采用 CC - BY - 4.0 许可证。